suppressPackageStartupMessages({
library(ggplot2)
library(enrichMiR)
library(cowplot)
library(readxl)
library(S4Vectors)
})
theme_set(theme_cowplot(font_size = 12))
CDplot <- function (ll, by = NULL, k = 3, breaks = NULL, sameFreq = FALSE,
addN = FALSE, dig.lab = NULL, minN = 10, title="", ...)
{
library(ggplot2)
if (!is.list(ll)) {
if (is.null(by))
stop("If `ll` is not already a list, `by` should be given.")
if (length(by) != length(ll))
stop("Lengths of ll and by differ.")
w <- which(!is.na(by) & !is.na(ll))
by <- by[w]
ll <- ll[w]
if (is.factor(by) || is.logical(by) || length(unique(by)) <
7) {
ll <- split(ll, by)
}
else {
if (is.null(dig.lab))
dig.lab <- max(c(2, 3 - ceiling(log10(abs(mean(by))))))
if (is.null(breaks))
breaks <- k
if (sameFreq) {
k <- k + 1
breaks <- unique(quantile(by, prob = seq(from = 0,
to = 1, length.out = k), na.rm = TRUE))
if (length(breaks) < k)
breaks <- unique(quantile(c(0, by[by != 0]),
prob = seq(from = 0, to = 1, length.out = k),
na.rm = TRUE))
if (length(breaks) < k) {
desiredK <- k
breaks <- 1
while (length(breaks) < desiredK && k < 100) {
k <- k
breaks <- unique(quantile(by, prob = seq(from = 0,
to = 1, length.out = k), na.rm = TRUE))
}
}
}
ll <- split(ll, cut(by, breaks, dig.lab = dig.lab))
}
}
ll <- enrichMiR:::.mergeSmallerGroups(ll, minN = minN)
print(paste(title, ks.test(ll[[1]],rev(ll)[[1]])$p.value))
d <- dplyr::bind_rows(lapply(ll, FUN = function(x) {
data.frame(y = (seq_along(x) - 1)/(length(x) - 1), x = sort(x))
}), .id = "Sets")
d$Sets <- factor(d$Sets, levels = unique(d$Sets))
if (addN)
levels(d$Sets) <- paste0(levels(d$Sets), " (n=", as.numeric(table(d$Sets)),
")")
p <- ggplot(d, aes(x, y, colour = Sets)) + geom_vline(xintercept = 0,
linetype = "dashed") + geom_line(...)
p + ylab("Cumulative proportion") + ggtitle(title)
}
df <- list( sperm=read.delim("sperm_longRNAseq_Kathi_molecular_psychiatry_2018.csv",header=TRUE,row.names=1),
zygote=read.delim("zygote_kathi_MSUS_Control.csv",header=TRUE,row.names=1))
e <- as.data.frame(read_excel("embryos_diff_analysis_results_dream.xlsx"))
e <- e[!duplicated(e$symbol) & !is.na(e$symbol),]
row.names(e) <- e$symbol
df$embryo <- e
df <- lapply(df, FUN=enrichMiR:::.homogenizeDEA)
mirs <- c("mmu-miR-871-3p","mmu-miR-31-5p","mmu-miR-155-5p","mmu-miR-878-5p","mmu-miR-34c-5p",
"mmu-miR-202-5p","mmu-miR-15a-5p","mmu-miR-329-3p","mmu-miR-335-5p","mmu-miR-206-3p")
Using targetScan (all sites)
ts <- readRDS("/mnt/schratt/enrichMiR_data/Targetscan/20201102_Targetscan_Mouse_AllSites_mouse.rds")
fam <- setNames(metadata(ts)$families[mirs], mirs)
getVals <- function(x, set, ts, val="score"){
x <- x[x %in% levels(ts$feature)]
y <- setNames(rep(0,length(x)),x)
ts <- ts[ts$set==set,]
y[as.character(ts$feature)] <- ts[[val]]
y
}
doplot <- function(ts, set, e, val="score", ...){
by <- getVals(row.names(e),set,ts,val=val)
lfc <- setNames(e[names(by),"logFC"],names(by))
CDplot(lfc, by=by, title=names(set), ...)
}
Zygotes
Sites
xtit <- "Zygote logFC"
p <- lapply(names(fam), FUN=function(x){
doplot(ts, fam[x], df$zygote, breaks=c(0,1,3,10), val="sites", size=1.4) +
labs(x=xtit, colour="NbSites", y="") + xlim(-1.5,1.5) +
theme(legend.position = c(0.7, 0.25))
})
## [1] "mmu-miR-871-3p 0.0342151429355011"
## [1] "mmu-miR-31-5p 0.613228290095045"
## [1] "mmu-miR-155-5p 0.185504507971212"
## [1] "mmu-miR-878-5p 0.234714739481151"
## [1] "mmu-miR-34c-5p 0.802356614955881"
## [1] "mmu-miR-202-5p 0.900259248081046"
## [1] "mmu-miR-15a-5p 0.0184688874555341"
## [1] "mmu-miR-329-3p 0.138730281258961"
## [1] "mmu-miR-335-5p 0.624002811028473"
## [1] "mmu-miR-206-3p 0.736950894383832"
plot_grid(plotlist = p, nrow = 5)
Scores
p <- lapply(names(fam), FUN=function(x){
doplot(ts, fam[x], df$zygote, k=3, sameFreq=TRUE, val="score", size=1.4, dig.lab=2) +
labs(x=xtit, colour="context score", y="") + xlim(-1.5,1.5) +
theme(legend.position = c(0.65, 0.25))
})
## [1] "mmu-miR-871-3p 0.0205082081103048"
## [1] "mmu-miR-31-5p 7.66294513373511e-05"
## [1] "mmu-miR-155-5p 0.00502056115320393"
## [1] "mmu-miR-878-5p 0.0730000579082727"
## [1] "mmu-miR-34c-5p 2.51711099861218e-05"
## [1] "mmu-miR-202-5p 0.0853739146546955"
## [1] "mmu-miR-15a-5p 3.00267444208746e-06"
## [1] "mmu-miR-329-3p 4.01484182122047e-07"
## [1] "mmu-miR-335-5p 9.32261081387775e-06"
## [1] "mmu-miR-206-3p 0.0485324757514297"
plot_grid(plotlist = p, nrow = 5)
Embryo
Sites
xtit <- "Embryo logFC"
p <- lapply(names(fam), FUN=function(x){
doplot(ts, fam[x], df$embryo, breaks=c(0,1,3,10), val="sites", size=1.4) +
labs(x=xtit, colour="NbSites", y="") + xlim(-1.5,1.5) +
theme(legend.position = c(0.7, 0.25))
})
## [1] "mmu-miR-871-3p 0.325040996515913"
## [1] "mmu-miR-31-5p 0.334803753228905"
## [1] "mmu-miR-155-5p 0.54005795621705"
## [1] "mmu-miR-878-5p 0.573451539159775"
## [1] "mmu-miR-34c-5p 0.893506964275063"
## [1] "mmu-miR-202-5p 0.978854781784588"
## [1] "mmu-miR-15a-5p 0.459860310876121"
## [1] "mmu-miR-329-3p 0.273840615554548"
## [1] "mmu-miR-335-5p 0.371802299927641"
## [1] "mmu-miR-206-3p 0.66346132793046"
plot_grid(plotlist = p, nrow = 5)
Scores
p <- lapply(names(fam), FUN=function(x){
doplot(ts, fam[x], df$embryo, k=3, sameFreq=TRUE, val="score", size=1.4, dig.lab=2) +
labs(x=xtit, colour="context score", y="") + xlim(-1.5,1.5) +
theme(legend.position = c(0.65, 0.25))
})
## [1] "mmu-miR-871-3p 0.260165431701375"
## [1] "mmu-miR-31-5p 0.233549187810498"
## [1] "mmu-miR-155-5p 0.489237014247127"
## [1] "mmu-miR-878-5p 0.52801464250823"
## [1] "mmu-miR-34c-5p 0.134093329167522"
## [1] "mmu-miR-202-5p 0.716073281130784"
## [1] "mmu-miR-15a-5p 0.98590105575504"
## [1] "mmu-miR-329-3p 0.938184258722547"
## [1] "mmu-miR-335-5p 0.0812785936984055"
## [1] "mmu-miR-206-3p 0.810467570384735"
plot_grid(plotlist = p, nrow = 5)
Sperm
Sites
xtit <- "Sperm logFC"
p <- lapply(names(fam), FUN=function(x){
doplot(ts, fam[x], df$sperm, breaks=c(0,1,3,10), val="sites", size=1.4) +
labs(x=xtit, colour="NbSites", y="") + xlim(-1.5,1.5) +
theme(legend.position = c(0.7, 0.25))
})
## [1] "mmu-miR-871-3p 0.320834866436564"
## [1] "mmu-miR-31-5p 0.675186896540313"
## [1] "mmu-miR-155-5p 0.309005294272651"
## [1] "mmu-miR-878-5p 0.484391138972185"
## [1] "mmu-miR-34c-5p 0.310277797164972"
## [1] "mmu-miR-202-5p 0.319836749081971"
## [1] "mmu-miR-15a-5p 0.0389228575661364"
## [1] "mmu-miR-329-3p 0.19763177206496"
## [1] "mmu-miR-335-5p 0.0349573913564567"
## [1] "mmu-miR-206-3p 0.357935413734141"
plot_grid(plotlist = p, nrow = 5)
Scores
p <- lapply(names(fam), FUN=function(x){
doplot(ts, fam[x], df$sperm, k=3, sameFreq=TRUE, val="score", size=1.4, dig.lab=2) +
labs(x=xtit, colour="context score", y="") + xlim(-1.5,1.5) +
theme(legend.position = c(0.65, 0.25))
})
## [1] "mmu-miR-871-3p 0.0129410737504753"
## [1] "mmu-miR-31-5p 0.035338751842581"
## [1] "mmu-miR-155-5p 1.87126523387704e-07"
## [1] "mmu-miR-878-5p 0.00119637317356347"
## [1] "mmu-miR-34c-5p 0.0112933600266281"
## [1] "mmu-miR-202-5p 0.0463311943031428"
## [1] "mmu-miR-15a-5p 3.19039389843345e-05"
## [1] "mmu-miR-329-3p 6.0317093465434e-06"
## [1] "mmu-miR-335-5p 0.000248128239410561"
## [1] "mmu-miR-206-3p 0.00183155804091462"
plot_grid(plotlist = p, nrow = 5)
Using targetScan (conserved sites)
ts <- readRDS("/mnt/schratt/enrichMiR_data/Targetscan/20201102_Targetscan_Mouse_ConSites_mouse.rds")
fam <- setNames(as.character(fam), names(fam))
fam <- fam[fam %in% levels(ts$set)]
Zygotes
Sites
xtit <- "Zygote logFC"
p <- lapply(names(fam), FUN=function(x){
doplot(ts, fam[x], df$zygote, breaks=c(0,1,3,10), val="sites", size=1.4) +
labs(x=xtit, colour="NbSites", y="") + xlim(-1.5,1.5) +
theme(legend.position = c(0.7, 0.25))
})
## [1] "mmu-miR-31-5p 0.184730832674956"
## [1] "mmu-miR-155-5p 0.469717158989"
## [1] "mmu-miR-34c-5p 0.887443015303102"
## [1] "mmu-miR-202-5p 0.0956097861893803"
## [1] "mmu-miR-15a-5p 0.806829167860812"
## [1] "mmu-miR-329-3p 0.379952695854162"
## [1] "mmu-miR-335-5p 0.177118083280719"
## [1] "mmu-miR-206-3p 0.0587536070269169"
plot_grid(plotlist = p, nrow = 4)
Scores
p <- lapply(names(fam), FUN=function(x){
doplot(ts, fam[x], df$zygote, k=3, sameFreq=TRUE, val="score", size=1.4, dig.lab=2) +
labs(x=xtit, colour="context score", y="") + xlim(-1.5,1.5) +
theme(legend.position = c(0.65, 0.25))
})
## [1] "mmu-miR-31-5p 0.18081059262906"
## [1] "mmu-miR-155-5p 0.479376787445864"
## [1] "mmu-miR-34c-5p 0.6208031996307"
## [1] "mmu-miR-202-5p 0.858423577067009"
## [1] "mmu-miR-15a-5p 0.063418821435808"
## [1] "mmu-miR-329-3p 0.210206949447425"
## [1] "mmu-miR-335-5p 0.0131355660200864"
## [1] "mmu-miR-206-3p 0.0936937909122906"
plot_grid(plotlist = p, nrow = 4)
Embryo
Sites
xtit <- "Embryo logFC"
p <- lapply(names(fam), FUN=function(x){
doplot(ts, fam[x], df$embryo, breaks=c(0,1,3,10), val="sites", size=1.4) +
labs(x=xtit, colour="NbSites", y="") + xlim(-1.5,1.5) +
theme(legend.position = c(0.7, 0.25))
})
## [1] "mmu-miR-31-5p 0.818879613958066"
## [1] "mmu-miR-155-5p 0.529987671062015"
## [1] "mmu-miR-34c-5p 0.179561621905284"
## [1] "mmu-miR-202-5p 0.125319600889517"
## [1] "mmu-miR-15a-5p 0.28142472309873"
## [1] "mmu-miR-329-3p 0.712352790003916"
## [1] "mmu-miR-335-5p 0.0825783640581742"
## [1] "mmu-miR-206-3p 0.356971007420054"
plot_grid(plotlist = p, nrow = 4)
Scores
p <- lapply(names(fam), FUN=function(x){
doplot(ts, fam[x], df$embryo, k=3, sameFreq=TRUE, val="score", size=1.4, dig.lab=2) +
labs(x=xtit, colour="context score", y="") + xlim(-1.5,1.5) +
theme(legend.position = c(0.65, 0.25))
})
## [1] "mmu-miR-31-5p 0.886429469545563"
## [1] "mmu-miR-155-5p 0.257566704961063"
## [1] "mmu-miR-34c-5p 0.143435217310236"
## [1] "mmu-miR-202-5p 0.753468906672615"
## [1] "mmu-miR-15a-5p 0.828867061480089"
## [1] "mmu-miR-329-3p 0.53935342705444"
## [1] "mmu-miR-335-5p 0.464007234930769"
## [1] "mmu-miR-206-3p 0.673987216735558"
plot_grid(plotlist = p, nrow = 4)
Sperm
Sites
xtit <- "Sperm logFC"
p <- lapply(names(fam), FUN=function(x){
doplot(ts, fam[x], df$sperm, breaks=c(0,1,3,10), val="sites", size=1.4) +
labs(x=xtit, colour="NbSites", y="") + xlim(-1.5,1.5) +
theme(legend.position = c(0.7, 0.25))
})
## [1] "mmu-miR-31-5p 0.0110563775122299"
## [1] "mmu-miR-155-5p 0.146243410377823"
## [1] "mmu-miR-34c-5p 0.933189705146401"
## [1] "mmu-miR-202-5p 5.15436043948547e-05"
## [1] "mmu-miR-15a-5p 0.165105125378995"
## [1] "mmu-miR-329-3p 0.0238179799952071"
## [1] "mmu-miR-335-5p 0.107576128934961"
## [1] "mmu-miR-206-3p 0.331041081113633"
plot_grid(plotlist = p, nrow = 4)
Scores
p <- lapply(names(fam), FUN=function(x){
doplot(ts, fam[x], df$sperm, k=3, sameFreq=TRUE, val="score", size=1.4, dig.lab=2) +
labs(x=xtit, colour="context score", y="") + xlim(-1.5,1.5) +
theme(legend.position = c(0.65, 0.25))
})
## [1] "mmu-miR-31-5p 0.341167832202413"
## [1] "mmu-miR-155-5p 0.0461552226468529"
## [1] "mmu-miR-34c-5p 0.232100664961071"
## [1] "mmu-miR-202-5p 0.232274970262658"
## [1] "mmu-miR-15a-5p 0.00306459832837036"
## [1] "mmu-miR-329-3p 0.0164378119541457"
## [1] "mmu-miR-335-5p 0.0353998693738633"
## [1] "mmu-miR-206-3p 0.000730312872537442"
plot_grid(plotlist = p, nrow = 4)
Candidate targets for miR-31-5p
Upregulated
ts1 <- readRDS("/mnt/schratt/enrichMiR_data/Targetscan/20201102_Targetscan_Mouse_AllSites_mouse.rds")
seed <- as.character(metadata(ts1)$families["mmu-miR-31-5p"])
ts1 <- ts1[ts1$set==seed,]
ts2 <- readRDS("/mnt/schratt/enrichMiR_data/Targetscan/20201102_Targetscan_Mouse_ConSites_mouse.rds")
ts2 <- ts2[ts2$set==seed,]
row.names(ts1) <- ts1$feature
ts1$conserved.sites <- 0
ts1[as.character(ts2$feature),"conserved.sites"] <- ts2$sites
colnames(ts1)[4] <- "targetscan.score"
df$embryo <- df$embryo[,-1:-7]
cands <- lapply(df, FUN=function(x){
m <- merge(x[x$PValue<0.05,],ts1, by.x="row.names", by.y="feature")
m <- m[order(-(
2*abs(m$targetscan.score) + m$logFC - log10(m$FDR) + as.numeric(m$conserved.sites>0) )),]
row.names(m) <- m$Row.names
m <- m[m$targetscan.score < 0.1,]
m$t <- m$Row.names <- m$set <- m$agg <- m$z.std <- NULL
for(f in colnames(m)){
if(is.numeric(m[[f]]) && !is.integer(m[[f]]))
m[[f]] <- dround(m[[f]], 2, TRUE)
}
as.data.frame(m)
})
saveRDS(cands, file="candidate_targets.rds")
lapply(cands, head)
## $sperm
## logFC meanExpr PValue FDR sites targetscan.score conserved.sites
## Glul 1.00 7.7 2.4e-06 0.0042 2 -0.60 1
## Pik3c2a 1.00 4.2 3.0e-06 0.0048 2 -0.36 2
## Trank1 0.93 4.7 6.9e-05 0.0240 3 -0.53 1
## Wdr5 1.00 1.8 2.1e-03 0.1000 2 -0.65 1
## Eif5 0.93 5.2 8.4e-05 0.0260 1 -0.39 1
## Bicd1 1.10 3.7 1.1e-04 0.0280 2 -0.23 1
##
## $zygote
## logFC meanExpr PValue FDR sites targetscan.score
## Ugt1a5 2.20 -2.90 0.01600 0.12 1 -0.41
## Tmprss11f 0.96 -5.00 0.02400 0.12 3 -0.51
## Rsbn1 0.48 3.40 0.01900 0.12 3 -0.67
## A530084C06Rik 2.30 -3.10 0.00081 0.12 2 -0.27
## Rab29 0.63 0.16 0.04500 0.12 2 -0.54
## Fndc5 0.92 3.20 0.03400 0.12 1 -0.39
## conserved.sites
## Ugt1a5 0
## Tmprss11f 1
## Rsbn1 2
## A530084C06Rik 0
## Rab29 1
## Fndc5 1
##
## $embryo
## logFC AveExpr PValue FDR sites targetscan.score conserved.sites
## Snx22 0.97 2.90 0.0260 1 2 -0.25 2
## Ccar2 1.50 0.78 0.0220 1 2 -0.49 0
## Cep85 0.78 4.20 0.0150 1 1 -0.18 1
## Abcb6 1.60 2.00 0.0017 1 1 -0.27 0
## Ppp3ca 0.63 5.10 0.0390 1 1 -0.25 1
## Kdm2b 0.36 5.10 0.0099 1 1 -0.36 1
Downregulated
lapply(cands, FUN=function(m){
m <- m[order(-(
2*abs(m$targetscan.score) - m$logFC - log10(m$FDR) + as.numeric(m$conserved.sites>0) )),]
head(m,10)
})
## $sperm
## logFC meanExpr PValue FDR sites targetscan.score conserved.sites
## Cxcl12 -1.5 2.20 2.6e-05 0.014 1 -0.590 1
## Slc17a8 -2.9 -2.80 3.5e-03 0.120 1 -0.190 1
## Vipr2 -2.8 -3.90 1.5e-04 0.033 2 -0.350 0
## Mrap2 -3.2 -2.40 2.0e-04 0.037 1 -0.130 0
## Itga5 -2.1 -1.00 1.4e-04 0.032 1 -0.140 1
## Cd28 -1.9 -1.70 3.8e-03 0.130 2 -0.450 1
## Gpr179 -2.5 -1.50 1.6e-03 0.094 1 -0.038 1
## Psmb11 -2.7 -2.60 7.9e-04 0.072 2 -0.340 0
## Tpsb2 -2.7 -3.80 6.7e-04 0.066 1 -0.310 0
## Slc10a2 -2.7 -0.99 7.4e-05 0.025 1 -0.085 0
##
## $zygote
## logFC meanExpr PValue FDR sites targetscan.score conserved.sites
## Hsf2bp -2.30 -1.60 0.0300 0.12 1 -0.460 1
## Satb2 -1.90 -1.60 0.0110 0.12 2 -0.380 2
## Dpep3 -2.50 -2.90 0.0029 0.12 1 -0.019 1
## Jazf1 -1.20 -0.23 0.0015 0.12 2 -0.640 1
## Ret -1.90 -1.80 0.0100 0.12 2 -0.250 1
## Wdr5 -0.93 2.50 0.0360 0.12 2 -0.650 1
## Snx22 -1.70 -1.30 0.0067 0.12 2 -0.250 2
## Rab9b -1.20 -4.30 0.0380 0.12 2 -0.490 1
## Nr5a2 -1.50 1.30 0.0140 0.12 1 -0.320 1
## Plin1 -2.70 -0.62 0.0290 0.12 1 -0.210 0
##
## $embryo
## logFC AveExpr PValue FDR sites targetscan.score conserved.sites
## Plekhg2 -1.80 -0.470 0.00078 1 1 -0.007 0
## Cacnb2 -0.49 4.600 0.02300 1 2 -0.160 1
## Shank1 -0.96 -0.029 0.02300 1 1 -0.320 0
## Purg -1.10 1.100 0.02500 1 1 -0.230 0
## Tmc2 -1.40 0.890 0.03700 1 1 -0.049 0
## Klhl18 -0.97 2.200 0.00900 1 3 -0.230 0
## Kdm2b 0.36 5.100 0.00990 1 1 -0.360 1
## Nr1h4 -1.30 0.480 0.00650 1 1 0.000 0
## Pqlc1 -0.37 6.100 0.01200 1 2 -0.460 0
## Etv5 -0.92 2.000 0.03700 1 1 -0.170 0
Session
sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so
##
## locale:
## [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
## [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
## [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
## [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] readxl_1.3.1 cowplot_1.1.1 enrichMiR_0.99.4
## [4] MASS_7.3-53.1 glmnet_4.1-1 Matrix_1.3-2
## [7] limma_3.46.0 shinydashboard_0.7.1 shiny_1.6.0
## [10] data.table_1.14.0 S4Vectors_0.28.1 BiocGenerics_0.36.0
## [13] ggplot2_3.3.3
##
## loaded via a namespace (and not attached):
## [1] fgsea_1.16.0 colorspace_2.0-0
## [3] ellipsis_0.3.1 XVector_0.30.0
## [5] GenomicRanges_1.42.0 farver_2.1.0
## [7] DT_0.17 bit64_4.0.5
## [9] AnnotationDbi_1.52.0 fansi_0.4.2
## [11] xml2_1.3.2 codetools_0.2-18
## [13] splines_4.0.3 cachem_1.0.4
## [15] knitr_1.31 jsonlite_1.7.2
## [17] Rsamtools_2.6.0 dbplyr_2.1.0
## [19] GO.db_3.12.1 geneLenDataBase_1.26.0
## [21] compiler_4.0.3 httr_1.4.2
## [23] assertthat_0.2.1 fastmap_1.1.0
## [25] later_1.1.0.1 htmltools_0.5.1.1
## [27] prettyunits_1.1.1 tools_4.0.3
## [29] gtable_0.3.0 glue_1.4.2
## [31] GenomeInfoDbData_1.2.4 dplyr_1.0.5
## [33] rappdirs_0.3.3 fastmatch_1.1-0
## [35] Rcpp_1.0.6 Biobase_2.50.0
## [37] cellranger_1.1.0 jquerylib_0.1.3
## [39] vctrs_0.3.6 Biostrings_2.58.0
## [41] nlme_3.1-152 rtracklayer_1.50.0
## [43] iterators_1.0.13 xfun_0.22
## [45] stringr_1.4.0 mime_0.10
## [47] lifecycle_1.0.0 shinycssloaders_1.0.0
## [49] XML_3.99-0.6 scam_1.2-10
## [51] zlibbioc_1.36.0 scales_1.1.1
## [53] hms_1.0.0 promises_1.2.0.1
## [55] MatrixGenerics_1.2.1 SummarizedExperiment_1.20.0
## [57] curl_4.3 yaml_2.2.1
## [59] memoise_2.0.0 goseq_1.42.0
## [61] gridExtra_2.3 sass_0.3.1
## [63] biomaRt_2.46.3 stringi_1.5.3
## [65] RSQLite_2.2.4 highr_0.8
## [67] zetadiv_1.2.0 foreach_1.5.1
## [69] GenomicFeatures_1.42.2 BiocParallel_1.24.1
## [71] shape_1.4.5 GenomeInfoDb_1.26.4
## [73] rlang_0.4.10 pkgconfig_2.0.3
## [75] bitops_1.0-6 matrixStats_0.58.0
## [77] evaluate_0.14 lattice_0.20-41
## [79] purrr_0.3.4 labeling_0.4.2
## [81] GenomicAlignments_1.26.0 htmlwidgets_1.5.3
## [83] bit_4.0.4 tidyselect_1.1.0
## [85] magrittr_2.0.1 R6_2.5.0
## [87] IRanges_2.24.1 generics_0.1.0
## [89] DelayedArray_0.16.2 DBI_1.1.1
## [91] pillar_1.5.1 aggregation_1.0.1
## [93] withr_2.4.1 mgcv_1.8-34
## [95] survival_3.2-10 RCurl_1.98-1.3
## [97] tibble_3.1.0 crayon_1.4.1
## [99] utf8_1.2.1 BiocFileCache_1.14.0
## [101] rmarkdown_2.7 progress_1.2.2
## [103] grid_4.0.3 blob_1.2.1
## [105] digest_0.6.27 xtable_1.8-4
## [107] httpuv_1.5.5 openssl_1.4.3
## [109] munsell_0.5.0 BiasedUrn_1.07
## [111] bslib_0.2.4 askpass_1.1